Occupancy sensors have the potential to significantly reduce energy
use by switching off electrical loads when a normally occupied area
is vacated. While occupancy sensors can be used to control a variety
of load types, their most popular use has been to control lighting
in commercial buildings. Manufacturers claim savings of 15% to 85 %,
although there is little published research to support the magnitude
or timing of reductions. Energy savings and performance are directly
related to the total wattage of the load being controlled, effectiveness
of the previous control method, occupancy patterns within the space
and proper sensor commissioning. In an effort to measure performance,
energy savings, and occupant acceptance, occupancy sensors were installed
in a small office building and two elementary schools. 15-minute data
was collected to assess performance. The three sites varied not only
in size but also by occupancy patterns, occupant density, and the previous
manual control strategies. Aggregate time-of-day lighting load profiles
are compared before and after the installation and throughout the commissioning
period when the sensors are tuned for optimum performance. For instance,
savings on weekdays in the office building were less than 10% prior
to the commissioning, although nearly doubled by proper tuning of the
time delay setting and correcting false triggering problems. False "ons" during
evening hours also affected savings. Occupant acceptance, sensor performance,
and commissioning aspects are discussed as well as some recommendations
for improved performance.

Introduction

Occupancy sensors (sometimes called motion sensors) replace conventional
light switches with automatic controls that turn fixtures on and off
based on the presence or absence of occupants in a controlled space.
Two primary detection technologies are most common: passive infrared
and ultrasonic sensing. Passive infrared sensors (PIRs) detect the
difference in long-wave radiation between objects and their background.
A compound lens in each fixture divides the coverage area into triangular
zones: when the infrared temperature in a zone changes (such as that
produced by a persons hand) this is interpreted as movement and the
lighting system is kept on. If no motion is sensed over a given time
delay (typically adjustable from 30 seconds to 30 minutes) the lighting
system is turned off. However, PIR technology requires an open line
of sight so partitions and furniture may block proper operation (e.g.,
they are not generally appropriate to control lighting in bathroom
spaces). Multiple sensors must be used in large spaces and ceiling
mounting is required for many space configurations for effective sensing.
False triggering may occur when units are placed near HVAC vents due
to the temperature change of the surroundings in the sensors field
of view.

Ultrasonic sensor (US) types emit low intensity inaudible high frequency
(20 to 40 kHz) sound waves to detect motion from the changing return
echo patterns. When the space "acoustic signature" is altered
the device maintains power to the lighting system. However, if no motion
is detected over a set time delay, the lights are switched off. US
sensors can cover large enclosed area with partitions well, however,
they may be sensitive to false triggering from non-human motion (e.g.
air motion, ceiling fans, etc.).

A final type of occupancy sensor, so called hybrid or "dual technology" units,
use both PIR and ultrasonic sensing to provide more reliable
occupant detection. Such devices can nearly eliminate false triggering
with greater sensitivity while making it possible to incorporate
shorter time delays. These devices, however, have premium prices.
Also very short time delays and excessive switching may be distracting
to occupants in adjacent spaces and can adversely affect lamp
and ballast life. Parasitic power consumption of occupancy sensors
is low. Electrical demand is generally 0.2 W per sensor for ultrasonic
types and 0.002 W for PIR models (Puleo 1991). Specific control
capabilities in terms of coverage sensitivity adjustment, time delays,
and user control method vary considerably from one manufacturer to
the next. A more complete description of the operational characteristics
and performance of specific models of occupancy sensors is contained
in a specifier report available from the National Lighting Product
Information Program (NLPIP 1992).

Previous Experience and Research

As an inexpensive option and potential retrofit measure, occupancy
sensors appeal to building managers. On the other hand, problems with
early installations have damaged the reputation of the technology for
some users (Puleo 1991). Anecdotal reports suggested that older products
failed regularly or turned off lights on occupied classrooms, requiring
extra maintenance and/or causing frustration for users. The most frequently
cited problems with performance involve false triggering from misinterpretation
of space occupancy. This includes "false positives" in which
the device triggers on, but no one is present and "false negatives" where
lights are turned off when the space is occupied. False positives lead
to wasted lighting energy use, while false negatives can greatly reduce
the user acceptance of occupancy based lighting controls.

In discussion with educational facilities planners, many questioned
the economics of occupancy sensors. They argued that classrooms, which
make up the bulk of primary and secondary school facilities, do not
remain unoccupied for long periods and that most teachers diligently
turn lights off upon leaving rooms. A number expressed the opinion
that occupancy sensors would be most appropriate for intermittently
used spaces, such as break and copy rooms. However, without empirical
evidence, the performance and economics of the technology remained
the subject of speculation.

Although occupancy sensors have been used in many commercial facilities
over the last decade, published third party performance data is surprisingly
sparse (Piette 1995). Both the Electric Power Research Institute (EPRI)
and American Society of Heating, Refrigerating, and Air-Conditioning
Engineers (ASHRAE) estimate an average 30% savings from this technology
in generic assessments for commercial buildings (EPRI 1993; ASHRAE
1989). These data are supported by a utility evaluation by Consolidated
Edison which found a 30% reduction in average lighting demand for its
projects which installed occupancy sensors (Audin 1993).

Measured data from case studies suggest that good performance from
occupancy sensor installations can be realized. A retrofit of an office
building with passive infrared occupancy sensor controls in South Australia
yielded a 40% reduction in lighting energy use with a simple payback
of two years (Caddet 1995). Also, several case studies of occupancy
sensor installations show savings of 25 to 75% in variety of spaces
(EPRI 1994). Finally, a detailed study of occupancy sensors used in
a national laboratory found a 31% average lighting energy reduction
(Richman et al. 1995) with savings strongly affected both by space
type and time delay setting. Savings were highest for mixed ownership
spaces (e.g. lunchrooms, copy rooms, restrooms etc.) and lowest for
administrative areas. Savings were more than doubled by reducing time
delays from the manufacturer recommended settings of 10 to 20 minutes
to 2.5 minutes.

Performance data specific to educational facilities is limited. Occupancy
sensor manufacturers often claim a 40 to 50% savings in classroom energy
use in product literature. A pertinent case study at the University
of New Hampshire, showing a reduction in classroom lighting system
on-time of some 3 hours per day (EPRI 1994). Researchers at Rensselaer
Polytechnic Institute performed field surveys at an elementary school
and junior high school to determine classroom occupancy patterns and
to estimate wasted lighting energy using a methodology described by
Rae and Jaekel (1987). In the elementary school the estimated weekly
lighting energy use was 1,694 kWh with some 416 kWh or 25% of the total
being wasted when no occupants were present (NLPIP 1992). In the junior
high school the wasted lighting energy averaged 15%.

However, prior to this study no evaluation had examined the savings
in a Florida classroom environment, and little information existed
on potential time-of-use impacts in buildings. In a study of energy
end-use intensities in Florida commercial buildings it was estimated
that interior lighting energy use accounted directly for 30% of all
energy use in office buildings and 32% of all consumption in Florida
schools (SRC 1992). Simulation studies indicate that internal lighting
is a large portion of the space cooling loads in commercial building,
leading to the possibility of indirect HVAC savings (Rundquist et al.
1993). Further, this same study identified advanced lighting controls
as a fruitful area for reducing commercial building energy use.

However, in all occupancy lighting control situations, the operation
of the lighting by the occupants emerges as the dominant factor in
determining potential lighting energy savings. Generally, lighting
energy reductions from occupancy sensors will roughly follow room vacancy
rates. Savings will be, of course, modified by occupant responsiveness
in turning off lights in unoccupied areas. Such behavior is also impossible
to evaluate within a laboratory environment. Thus, we desired to conduct
a series of tests of the technology using a "before and after" measurement
to determine actual potentials.

Building 200: Florida Solar Energy Center

As a first point of evaluation, we chose a small 5,000 ft2 office
building at the Florida Solar Energy Center's (FSEC) Cape Canaveral
facility. The lighting system for the entire building was metered,
comprising 29 private and suite offices. The building's lighting system
consisted of two and four tube fluorescent fixtures with T-12 lamps
with magnetic ballasts with an installed power density of 1.95 W/ft2.
Calibrated power transducers on the 277 volt lighting circuits sent
watt-hour pulses to the data logger. A multi-channel data logger was
used to record the data, with scans taken every ten seconds and integrated
averages and totals sent to storage every 15 minutes. The data was
automatically relayed to a mainframe computer via modem and dedicated
telephone line each evening for plotting and review by the project
engineer the following morning.

Six months of pre-retrofit lighting energy consumption data were taken.
The base line data indicated that annual lighting energy use in Building
200 was approximately 12,509 kWh. Twenty three PIR and US occupancy
sensors were installed in the facility on September 16, 1994. Each
occupancy sensor was mounted to provide good coverage of the controlled
zone-- both wall and ceiling mount devices were used. The lighting
controls were installed and configured using approximately 40 man-hours
of labor. Time delays were initially set to 15-minutes. Figure
1 compares two representative week day lighting demand profiles
before and after the occupancy sensor retrofit.

The influence of lights
accidentally left on is apparent in the pre-retrofit energy data as
is the switching during the lunch hour in the post installation period.

We then compared the long term pre-retrofit data to post-retrofit
data through January 1995 to assess energy savings as well as changes
in the daily pattern of consumption. Preliminary analysis of the weekday
lighting power data revealed moderate savings from 11 AM to 1 PM during
the lunch hour and from 5 PM to 7 AM. As shown in Figure
2, average daily weekday savings totaled approximately 7% (3.0
kWh/Day).

However, review of the data on individual days revealed power
use at odd late evening and early morning hours when the building was
believed to be unoccupied (see Figure 1). We suspected that this consumption
was due to false positives from malfunctioning occupancy sensors. The
manufacturer then suggested replacement of the three rogue sensors
with models less prone to false triggering.

After this was done, the problem was significantly reduced. However,
one puzzling bank of hallway fixtures continued to turn on during early
morning hours. At first a phantom 3 AM office visitor was suspected,
but after surveillance efforts failed to capture the culprit, suspicion
moved to a laser printer in the hallway. Apparently, the nightly movement
of paper from the printer was enough to trigger an ultrasonic sensor
in the same space. A reduction in the sensitivity setting and relocation
of the device solved the problem. However, the metering information
we had available for trouble-shooting makes our case unique and we
suspect that in most installations such problems can go undetected.
After solving these difficulties, metered average savings rose to approximately
10%.

We then reset the time delays in the occupancy sensors to seven minutes
on April 19th, 1995 and continued to record lighting energy use data
for another three months from April 20th to July 7th. As shown by Figure
2, the average daily energy savings on work days were nearly doubled
to approximately 19% (8.25 kWh/Day) by correction of false triggering
and decrease of the sensor time delay.

Unexpectedly, we found weekend power consumption to be slightly greater
after the retrofit. This was primarily due to the extra time the lights
stayed on after the room was vacated, while before the retrofit, weekend
visitors probably switched off lights immediately upon exit. Also,
weekend workers moving through the building were found to activate
many more lights than they would have turned on with manual controls.
However, impact on annual energy use was negligible since non-work
day lighting energy use was only a small fraction of the annual total.

The project had a final direct lighting energy savings of approximately
2,060 kWh per year and approximately 2,580 kWh when HVAC cooling savings
were added (Rundquist et al. 1993). Estimated cost savings were approximately
$129 per year. This matches against the $2,354 spent on the sensors
and their installation. The project did not show attractive economics
(a 18.2 year payback), but was not intended to be cost effective, but
rather to allow study of the factors that affect occupancy sensor retrofit
performance on a small scale installation. The loads being controlled
by each sensor were fairly small; obviously it is advantageous to control
the largest possible load with each device. Also, energy researchers
are likely more vigilant in their operation of lighting than typical
office personnel so that savings from this installation were not expected
to reflect a typical installation.

An occupant acceptance survey was administered to those receiving
occupancy sensors in the study. The survey revealed good overall acceptance
of the sensors throughout the monitoring period with incidences of
false negatives. The only drawback observed was a slight increase in
the frequency of ballast and lamp failures. However, the ballasts were
estimated to be at least twenty years old and many of the lamps were
near the end of their useful life. Thus, proper commissioning of occupancy
sensors emerged as a key issue in achieving reasonable performance.

Northwest Elementary School, Pasco County,
Florida

Northwest Elementary School, located on the west coast of Florida,
was the location of the first school site that was evaluated. A 58,000
ft2 main building, comprised of classroom pods, administrative spaces,
a media center, and a cafeteria, became the subject of the study. The
work was sponsored by the Florida Department of Education and is more
fully described in a source report (Floyd et al. 1995).

Primary lighting for the school was 2 x 4 luminaires with T10 lamps/magnetic
ballast or T8 lamps/electronic ballast. The connected facility
lighting load is approximately 87 kW. The test building was unusual
in that it already contained an efficient lighting system. Pasco County
also has one of the most aggressive energy management programs of any
district school board in the state. Even before installation of the
occupancy sensors, lighting was effectively controlled by facility
staff so as to prevent waste. Given these factors, it was expected
that the evaluation in Northwest Elementary would provide insight into
the minimum savings that could be expected from the technology if properly
applied in a Florida school.

Technicians audited the school on December 21, 1994 and subsequently
drew up a plan for instrumentation to monitor its energy use. The facility
was instrumented on February 25, 1995, also using a similar before
and after monitoring protocol. Fifteen minute electrical demand data
were taken for six months prior to the lighting controls being modified
to accommodate occupancy sensors. Data in the baseline period revealed
that lighting made up approximately 24% of total electrical energy
use at the school (70 kBtu/ft2).

A total of 46 passive infrared (PIR) occupancy sensors were installed
and carefully adjusted in terms of location, time delay, and sensing
sensitivity from August 7, 1995 to August 15, 1995. The installation
was performed by a team of two electricians, a Research Engineer and
the Energy Coordinator from Pasco County. Approximately 33 classrooms,
seven offices, and a cafeteria were equipped with occupancy sensors.
In several offices wall sensors were used. The remainder of the spaces
(classrooms, cafeteria, and larger offices) received ceiling mounted
sensors. The broad coverage of the ceiling mounted PIR sensors minimized
the need for multiple occupancy sensors in all but five areas. Dual
technology sensors were considered, but not utilized due to their higher
cost.

Classroom occupancy sensors were located in a corner near the teachers
desk to minimize false "offs" when only the teacher was in
the classroom. All occupancy sensors were set to a 10 minute time delay,
which has worked well in most situations. Shorter time delays may improve
savings, however, false "offs" may also increase. Past
installation experience has shown that unless the occupancy sensors
are properly located, aimed, and tested by experienced personnel, poor
savings and occupant dissatisfaction will result.

The analysis of the comparative pre- and post-retrofit periods as
shown in Figure
3, indicated an average savings of 10.8% (96 kWh) on school days
of the pre-retrofit lighting energy with greater reductions to total
energy due to reduced load on the air conditioning system. Most of
the savings occurred during the evening hours so that monthly peak
electrical demand was unaffected.

There are some 200 school days per
year, not including holidays, weekends and summer recess. The school
day extends from 7:00 AM to 3:45 PM, although office and janitorial
activities often extend beyond the formal school day schedule when
much of the savings were found to accrue.

Based on the monitoring, an annual direct lighting energy savings
of 26,620 kWh was estimated. To this was added an estimated additional
7,260 kWh in reduced HVAC costs (Rundquist et al. 1993). At the facility's
electricity rate ($0.05/kWh) annual monetary savings is estimated at
$1,694. The data did not evidence any reduction in peak electrical
demand from the retrofit, so no credit was taken for this portion of
monthly energy costs.

The cost for the sensors, wiring and relay packs for the project was
$4,067 or about $88 per control. Installation labor was valued at $2,000
(125 man-hours). Including costs of installation and set-up, the payback
of the occupancy sensor retrofit was approximately 3.6 years with a
28% simple rate of return from the investment. This performance is
considered excellent given that the building already had an efficient
lighting system which was responsibly controlled prior to the occupancy
sensor installation. The project results indicate that with proper
installation and adjustment (which was found to be critically important
to user acceptance and performance) occupancy sensor technology can
provide economically attractive returns either in new or existing educational
facilities.

Fellsmere Elementary School

The third project in which occupancy sensors were installed is an
elementary school in Central Florida which is serving as a pilot project
to demonstrate energy savings in public buildings similar to that achieved
by the Texas LOANSTAR project (Verdict et al. 1990). Termed FLASTAR
(Florida Alliance for Saving Taxes and Resources), the project has
entailed the comprehensive metering of a Florida elementary school
with which to demonstrate energy savings potential. Over twenty channels
of weather and sub-metered energy data has been collected since April
12, 1995.

The facility is composed of the main school building, with an attached
new wing and various portable classroom areas. All school lighting
circuits are individually sub-metered so that this end-use can be separated. Figure
4 details the proportions of the sub-metered end uses from electricity
consumption data from April 12 to December 4, 1995 prior to the installation
of the occupancy sensors.

Metered lighting energy use has averaged
about 17% of total facility energy consumption.

The large "other" end-use category represents refrigeration,
kitchen cooking loads and miscellaneous end-uses such as computers,
office equipment, and water coolers. Measured electricity consumption
has totaled approximately 2,200 kWh on school days and 1,300 kWh on
non-school days. On this basis, annual estimated energy consumption
for the 35,000 square foot facility is approximately 75 kBtu/ft2. During
the summer of 1995, the first retrofit, replacement of aging chillers
was completed with an estimated 10% reduction to measured cooling energy
use at the facility (Sherwin et al. 1996).

The interior lighting system is predominantly from fluorescent fixtures.
Two-lamp fixtures based on the T-12 F34CW lamp with magnetic ballasts
are most common with 513 of this type and 133 of mixed one, three,
and four-tube fixtures. As audited, the connected lighting load is
59.0 kW or about 2.0 W/ft2. This compares to 1.4 W/ft2 for more contemporary
efficiency lighting systems for schools (McIvaine et al. 1994) and
suggests potential for improved controls. Audited classroom desk-top
illuminance levels were from 76 to 85 foot candles; each room is outfitted
with two wall switches that control one half of the classroom electrical
lighting.

Schedules strongly affect lighting energy consumption. The last day
of regular school occupancy for the Spring semester at Fellsmere Elementary
was on June 6, 1995. However, during the summer, some of the faculty
and secretaries were present from Monday to Thursday from 8 AM to 3
PM. Custodians were also on site from Monday to Thursday from 6:30
AM to 3:30 PM. Summer school was not held in the the portion of the
building metered in the project. The Fall school schedule resumed on
August 21 and continued until December 15 and faculty and staff remained
until December 22. Spring session commenced on January 3, 1996.

Since metered data showed lighting accounts for about 17% of electrical
end use at this facility, an occupancy sensor retrofit appeared to
possess considerable promise. The school staff appears to make efforts
to turn off lights after hours; however, there are numerous data to
show lights being inadvertently left on after hours and on weekends
(Sherwin 1996). A previous technical assistance report (TAR) and analysis
for the Institutional Buildings Program (IBP) had estimated a savings
for the retrofit of 25,960 kWh per year based on an assumed 20% reduction
in daily lighting hours at the facility (Bosek, Gibson and Associates
1995). Estimated project cost was $10,192 with a 4.1 year simple payback.

The occupancy sensors were installed on December 15th. A total of
59 controls were installed in the facility; 39 ceiling-mounted PIR
sensors were placed in classrooms and the 20 wall-mounted units were
installed in office and administrative locations. The total cost for
the sensors and hardware was similar to that at Northwest Elementary
$5,154 (or $87/control). However, the cost of labor for installation
was much higher at $9,365. The labor cost for the installation is difficult
to reconcile since the estimate shown by R.S. Means Mechanical Estimator
is only 3.5 hours per sensor installation-- an allowance which already
seems liberal given our experience at Northwest Elementary. The TAR
estimate for the retrofit labor was $3,803. The large disparity in
labor costs for the installation are currently unexplained.

The first analysis of the measured lighting load profile for school
days showed an increase of lighting electricity consumption of approximately
27% from 16.70 kWh/Day to 21.2 kWh per day, as shown in Figure
5.

Figure 5. Fellsmere Elementary Before and After the OC
Installation

On the other hand consumption on non-school days dropped by
20% from 6.91 kWh/Day to 5.53 kWh. Based on previous installation experience
we suspected that the sensors were poorly installed or improperly adjusted.

On February 22, 1996 the occupancy sensors were tuned in an effort
to increase the energy savings. Tuning consisted of reducing the time
delay from 12 minutes to approximately 7 minutes in most areas and
changing the program selection. The program dictates which technology
(ultrasonic and/or infrared) is used to initially turn on the lights
and which technology is used to keep the lights on. Prior to tuning,
either ultrasonic or infrared would turn the lights on. This was changed
to a setting where both technologies must detect movement in order
for the lights to come on. As shown in Figure 5, this resulted in an
improvement in performance, but still did not produce effective savings.

Although the tuning reduced the light energy use, usage was still
greater after the sensors were installed and tuned than with manual
switching. We suspect this is due to false positives occurring and
inadvertent tripping of the sensors when occupants enter the space
momentarily. The reasons for the poor initial performance seem to be
a combination of factors recently described by the county energy coordinator
(Aiken 1996). The specific controls installed were obtained through
a procurement process in which the lowest bidder was selected. The
acquired equipment was found to possess characteristics which may have
compromised performance. Based on examination of the data, it appears
as if a number of the ultrasonic sensors are falsely triggering during
evening hours, increasing consumption. Another cited complaint was
the long "strike time" of the sensors; once lights were turned
off, they would not turn back on for some 11 seconds. This led the
installation crew to alter the sensor set time delay in some locations
to the maximum available (15 minutes). As described above, both in
our studies and those performed by PNL (Richman et al. 1994), proper
setting of the device time delay is crucial to achieving potential
energy savings.

A further reduction to potential savings at the facility may be behavioral
(LaPointe 1996). Prior to installation of the control sensors, all
facility staff punctually turned off lights when leaving unoccupied
spaces. However, now staff leaves all occupancy sensor switches with
the room lights to be triggered on when an occupant enters spaces.
Based on observation by facility staff, lights are now on more of the
time in the average classroom than they were prior to the retrofit
since the typical space is left on for 7-minutes after it is vacated
until the occupancy sensor turns off the lighting. Also, even a momentary
visit by a single individual to a room or rooms in this configuration
will result in the lights being on for 7 minutes, whereas they would
likely not be powered at all in this instance under manual control.
Regardless, the failure in this case of the addition of occupancy sensors
to produce savings as installed, points to the importance of proper
specification of equipment, a careful installation and setup, and adequate
instruction to users. Such commission is critical to achieving expected
energy savings.

Conclusions

Occupancy sensors are frequently identified as an effective means
of controlling lighting energy costs in commercial buildings. However,
there are few field studies to support manufacturers estimates of energy
reductions. Realized savings depend upon human factors, previous control
strategies and proper sensor commissioning which can only be measured
in field studies.

In order to measure occupancy sensor performance, three sites were
monitored using a before and after monitoring protocol. The results
of the three case studies are summarized in Table 1. The first of these
case studies involved a small office building where a variety of sensors
were installed. Savings were first found to be moderate, but increased
significantly when sensor malfunctions were addressed and the time
delays were reduced. In the second case study an elementary school
was monitored for saving when PIR sensors were installed in classrooms,
a cafeteria, and administrative offices. A 10% savings was realized
even though the previous method of manual control was considered effective.
We expected the third site to produce similar results as site two since
the two schools had similar occupancy patterns. However, initial results
showed that the school was actually using more lighting energy in the
post period. The increase appears to be the result of poor sensor installation,
set-up, and user operation of the devices.

Table 1. Summary of Results from Three Case Studies of Occupancy
Sensor Retrofit

Site

Building 200

Northwest Elementary

Fellsmere Elementary

Building Type

Small office

Elementary school

Elementary school

Floor Area(Ft2)

5, 000

58,000

35,000

Lighting Load (kW)

9.7

110.0

59.0

Sensor Time Delay (min)

15 minutes (initial)

7 minutes (final)

10 minutes

12-15 minutes (initial)

7 minutes (final)

Baseline Annual Light kWh

12,509

12,509

246,481

108,004

Estimated Annual Savings kWh*

1,084

2,060

26,420

(-15,444)

Installed Cost

$2,354

$6,067

$15,446

Savings

10%

19%

11%

Negative

Payback (Yrs)

34.7

18.2

3.6

None

* These are direct savings. Total savings are approximately 25% greater
since HVAC interactions are included.

The results of the three case studies suggest that occupancy sensors
can provide savings in a variety of building types. However, savings
may vary greatly due to differences in occupancy patterns, previous
method of control and lighting load. In order to achieve good results,
it appears imperative to first determine the appropriateness of occupancy
sensors for a specific area over manual control with competing lighting
energy efficiency measures. Savings and user acceptance for areas selected
for control by occupancy sensors is influenced by proper sensor selection,
location and controls commissioning.

References

Aiken, J. (Indian River County School District). 1996. Personal communication
with D.S. Parker. February 13.